Results 11 to 20 of about 23,712,988 (293)
Dirichlet process mixture models for regression discontinuity designs
Statistical Methods in Medical Research, 2022 The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the ...
Federico Ricciardi +2 more
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Frontiers in Signal Processing, 2022 We consider the problem of a primary source tracking a moving object under time-varying and unknown noise conditions. We propose two methods that integrate sequential Bayesian filtering with transfer learning to improve tracking performance.
Omar Alotaibi +1 more
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A Dirichlet Process Mixture Model for Non-Ignorable Dropout [PDF]
Bayesian Analysis, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Moore, Camille M. +3 more
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Journal of Advanced Transportation, 2022 Interpretation of flexible cycling behavior has always been a tough task. It is meaningful to understand the overtaking behavior of cyclists for its threats to safety and its high frequency on shared roads.
Ailing Yin, Xiaohong Chen, Lishengsa Yue
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Scientific Reports, 2021 Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the ...
Sk Md Mosaddek Hossain +4 more
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Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures [PDF]
2006 9th International Conference on Information Fusion, 2006 Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on mixture of Dirichlet processes is introduced.
Caron, F +5 more
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A tutorial on Dirichlet process mixture modeling
Journal of Mathematical Psychology, 2019 Bayesian nonparametric (BNP) models are becoming increasingly important in psychology, both as theoretical models of cognition and as analytic tools. However, existing tutorials tend to be at a level of abstraction largely impenetrable by non-technicians.
Li, Yuelin +2 more
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Machinery Early Fault Detection Based on Dirichlet Process Mixture Model
IEEE Access, 2019 The most commonly used single feature-based anomaly detection method for the complex machinery, such as large wind power equipment, steam turbine generator sets, and reciprocating compressors, exhibits a defect of low-alarm accuracy due to the non ...
Bo Ma +4 more
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Frontiers in Energy Research, 2023 Growing server energy consumption is a significant environmental issue, and mitigating it is a key technological challenge. Application-level energy minimization strategies depend on accurate modeling of energy consumption during an application’s ...
Buhua Chen +4 more
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Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations [PDF]
, 2014 We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter (Formula presented.).
Silvia Liverani +5 more
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